



from __future__ import annotations

__UpperCAmelCase			  =			[]
def 						UpperCamelCase      (  snake_case__			:       list[list[int]]			,	snake_case__			:       int			,	snake_case__			:       int      )      ->	bool:
   for i in range(len(snake_case__      )      ):
      if board[row][i] == 1:
         return False
   for i in range(len(snake_case__      )      ):
      if board[i][column] == 1:
         return False
   for i, j in zip(range(snake_case__			,	-1			,	-1      )			,	range(snake_case__			,	-1			,	-1      )      ):
      if board[i][j] == 1:
         return False
   for i, j in zip(range(snake_case__			,	-1			,	-1      )			,	range(snake_case__			,	len(snake_case__      )      )      ):
      if board[i][j] == 1:
         return False
   return True
def 						UpperCamelCase      (  snake_case__			:       list[list[int]]			,	snake_case__			:       int      )      ->	bool:
   if row >= len(snake_case__      ):
      solution.append(snake_case__      )
      printboard(snake_case__      )
      print()
      return True
   for i in range(len(snake_case__      )      ):
      if is_safe(snake_case__			,	snake_case__			,	snake_case__      ):
         UpperCamelCase       :    int		      =      1
         solve(snake_case__			,	row + 1      )
         UpperCamelCase       :    Dict		      =      0
   return False
def 						UpperCamelCase      (  snake_case__			:       list[list[int]]      )      ->	None:
   for i in range(len(snake_case__      )      ):
      for j in range(len(snake_case__      )      ):
         if board[i][j] == 1:
            print('Q'			,	end=' '      )
         else:
            print('.'			,	end=' '      )
      print()


# n=int(input("The no. of queens"))
__UpperCAmelCase			  =			8
__UpperCAmelCase			  =			[[0 for i in range(n)] for j in range(n)]
solve(board, 0)


print('''The total no. of solutions are :''', len(solution))





import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Optional[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase      :  Optional[Any]       =   BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,    _lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Union[str, Any]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase    :   Any						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)